Entrega grupal
a) ¿Qué partido fue el ganador en los municipios con más de 100.000 habitantes (censo) en cada una de las elecciones?
| # b) ¿Qué partido fue el segundo cuando el primero fue el PSOE? ¿Y cuando el primero fue el PP? ` |
|---|
| # c) ¿A quién beneficia la baja participación? |
| ::: {.cell} |
| ```{.r .cell-code} election_tidy_with_participation <- election_tidy_with_siglas |> mutate(participacion = votos / censo) |
| participacion_partido <- election_tidy_with_participation |> group_by(anno, siglas) |> summarise( participacion_media = mean(participacion, na.rm = TRUE), votos_totales = sum(votos, na.rm = TRUE), .groups = “drop” ) |
| resultados_con_participacion <- participacion_partido |> mutate(resultado_votos = votos_totales / sum(votos_totales) * 100) |
| colores_partidos <- c( “PSOE” = “#E30000”, “PP” = “#0000FF”, “C’s” = “#FF6600”, “PNV” = “#006747”, “BNG” = “#0C6F6D”, “CIU” = “#9C3D28”, “UP” = “#6A0DAD”, “ERC” = “#E24D3A”, “EH-BILDU” = “#006F6F”, “MP” = “#00CFFF”, “VOX” = “#008000”, “OTROS” = “#BEBEBE” ) |
| # Crear la gráfica de dispersión con los colores definidos y mejor estética resultados <- ggplot(resultados_con_participacion, aes(x = participacion_media, y = resultado_votos, color = siglas)) + geom_point(size = 3, alpha = 0.7) + # Puntos con tamaño ajustado y transparencia geom_smooth(method = “lm”, se = FALSE, aes(group = siglas), color = “black”, linetype = “dashed”) + # Línea de regresión por partido facet_wrap(~ anno, scales = “free_y”) + # Facetas por año scale_color_manual(values = colores_partidos) + # Usar los colores definidos labs( title = “Relación entre Participación y Resultados Electorales por Año”, subtitle = “Cada punto representa un partido en una elección, con la participación y el porcentaje de votos”, x = “Participación Media (%)”, y = “Porcentaje de Votos (%)”, color = “Partido” ) + theme_minimal(base_size = 14) + # Estilo minimalista y tamaño de texto ajustado theme( legend.position = “bottom”, # Colocar la leyenda abajo legend.title = element_text(face = “bold”, size = 12), # Estilo de título de la leyenda legend.text = element_text(size = 10), # Estilo de texto de la leyenda strip.text = element_text(face = “bold”, size = 12), # Estilo de los títulos de las facetas plot.title = element_text(face = “bold”, size = 16, hjust = 0.5), # Estilo del título plot.subtitle = element_text(size = 12, hjust = 0.5) # Estilo del subtítulo ) |
| resultados_interactivo <- ggplotly(resultados) resultados_interactivo ``` |
| ::: {.cell-output-display} |
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| ::: ::: |
| ## ¿Cómo calibrar el error de las encuestas (recordemos que las encuestas son de intención de voto a nivel nacional)? |
| ::: {.cell} |
| ```{.r .cell-code} # Agrupar varios partidos a la misma sigla surveys_clean_siglas <- surveys_clean |> mutate(Partido=case_when( str_detect(Partido, “PSOE”) ~ “PSOE”, str_detect(Partido, “PP”) ~ “PP”, str_detect(Partido, “C’s”) ~ “C’s”, str_detect(Partido, “PNV”) ~ “PNV”, str_detect(Partido, “BNGO”) ~ “BNG”, str_detect(Partido, “CIU”) ~ “CIU”, str_detect(Partido, “UP”) ~ “UP”, str_detect(Partido, “ERC”) ~ “ERC”, str_detect(Partido, “EH-BILDU”) ~ “EH-BILDU”, str_detect(Partido, “MP”) ~ “MP”, str_detect(Partido, “VOX”) ~ “VOX”, TRUE ~ “OTROS”, )) # Calcular el porcentaje de votos por partido en las elecciones votes_percentage <- election_tidy_with_siglas |> group_by(anno, siglas) |> summarise(total_votes = sum(votos, na.rm = TRUE), .groups = “drop_last”) |> mutate(percentage_votes = total_votes / sum(total_votes) * 100) |> ungroup() |
| # Calcular el porcentaje de intención de voto por partido en las encuestas survey_percentage <- surveys_clean_siglas |> mutate(date_elec = year(date_elec)) |> group_by(date_elec, Partido) |> summarise(mean_intention = mean(value, na.rm = TRUE), .groups = “drop_last”) |> mutate(percentage_intention = mean_intention / sum(mean_intention) * 100) |> ungroup() |
| # Unir ambas tablas y calcular el error error_calibration <- votes_percentage |> inner_join(survey_percentage, by = c(“anno” = “date_elec”, “siglas” = “Partido”)) |> mutate(error = abs(percentage_votes - percentage_intention)) ``` ::: |
| ## ¿Qué casas encuestadoras acertaron más y cuáles se desviaron más de los resultados? |
| ::: {.cell} |
| ```{.r .cell-code} # Calcular el porcentaje de error por casa encuestadora media_errors <- surveys_clean_siglas |> mutate(anno = year(date_elec)) |> group_by(anno, Partido, media) |> summarise( mean_intention = mean(value, na.rm = TRUE), .groups = “drop” ) |> mutate(percentage_intention = mean_intention / sum(mean_intention) * 100) |> inner_join(votes_percentage, by = c(“anno”, “Partido” = “siglas”)) |> mutate(error = abs(percentage_votes - percentage_intention)) |
| # Calcular el error promedio por casa encuestadora accuracy_by_media <- media_errors |> group_by(media) |> summarise( mean_error = mean(error, na.rm = TRUE), .groups = “drop” ) |> arrange(mean_error) |
| # Identificar las casas encuestadoras más acertadas y más desviadas medios_mas_acertados <- accuracy_by_media |> slice_min(order_by = mean_error, n = 5) medios_menos_acertados <- accuracy_by_media |> slice_max(order_by = mean_error, n = 5) ``` ::: |
| ##Ejercicios extra Mendoza #Relación entre el censo y el voto mediante un mapa: |
| ::: {.cell} |
| ```{.r .cell-code} indice <- election_data|> filter(anno==2019) |> mutate(“key”=glue(“{codigo_provincia}-{codigo_municipio}”)) |> drop_na(votos_candidaturas, censo) |> mutate(“indice” = votos_candidaturas / censo) |
| mapa<-mapSpain::esp_get_munic() |> mutate(“key”=glue(“{cpro}-{cmun}”)) |
| mapa_indice<- mapa |> left_join(indice, by=“key”) |
| #Grafico de la participacion |
| g_ind<-ggplot(mapa_indice)+ geom_sf(aes(alpha=indice, fill=indice), color=NA)+ scale_alpha_continuous(range = c(0.7,0.9))+ scale_fill_gradient2(low = “#b9feff”,mid=“#00c9ff”, high = “#040b64”, midpoint = mean(indice$indice), labels = scales::label_number(scale = 100, suffix=“%”))+ labs(fill=“PARTICIPACION”, title = “PARTICIPACION 2019”)+ theme_minimal()+ theme( axis.text = element_blank(), # Eliminar etiquetas de los ejes axis.ticks = element_blank(), # Eliminar marcas de los ejes legend.position = “bottom”, # Colocar la leyenda abajo legend.title = element_text(face = “bold”, size = 12), # Estilo de título de la leyenda legend.text = element_text(size = 8, angle = 30), # Estilo de texto de la leyenda strip.text = element_text(face = “bold”, size = 12), # Estilo de los títulos de las facetas plot.title = element_text(face = “bold”, size = 12, hjust = 0.5), # Estilo del título plot.subtitle = element_text(size = 12, hjust = 0.5) # Estilo del subtítulo )+ guides(alpha = “none”) |
| #Grafico del censo de España g_cen<-ggplot(mapa_indice)+ geom_sf(aes(alpha=censo, fill=censo), color=NA)+ scale_alpha_continuous(range = c(0.7,0.9))+ scale_fill_gradient2(low = “#b9feff”,mid=“#00c9ff”, high = “#040b64”, midpoint = mean(election_data$censo), transform = “log”,labels = scales::label_number(scale = 1, accuracy = 1))+ labs(title = “CENSO EN 2019”)+ theme_minimal()+ theme( axis.text = element_blank(), # Eliminar etiquetas de los ejes axis.ticks = element_blank(), # Eliminar marcas de los ejes legend.position = “bottom”, # Colocar la leyenda abajo legend.title = element_text(face = “bold”, size = 12), # Estilo de título de la leyenda legend.text = element_text(size = 8, angle = 30), # Estilo de texto de la leyenda strip.text = element_text(face = “bold”, size = 12), # Estilo de los títulos de las facetas plot.title = element_text(face = “bold”, size = 12, hjust = 0.5), # Estilo del título plot.subtitle = element_text(size = 12, hjust = 0.5) # Estilo del subtítulo )+ guides(alpha = “none”) |
| votos_censo<-g_ind+g_cen #Sumamos las graficas para compararlas print(votos_censo) ``` |
::: {.cell-output-display}  ::: |
| ```{.r .cell-code} ganadores_mun<-election_tidy_with_siglas |> filter(anno==2019) |> mutate(“key”=glue(“{codigo_provincia}-{codigo_municipio}”)) |> group_by(key) |> drop_na(votos) |> slice_max(votos) |
| ganadores_mun<- ganadores_mun|> mutate(“Tipo_mun”= if_else(censo>median(ganadores_mun$censo),“Urbana”,“Rural”)) |
| ggplot(ganadores_mun)+ geom_bar(aes(x = siglas, fill = siglas))+ scale_fill_manual(values = colores_partidos)+ facet_wrap(~Tipo_mun)+ labs(title = “GANADORES RURALES Y URBANOS 2019”, x= “PARTIDOS”, y= “VICTORIAS”)+ theme_minimal()+ theme( legend.position = “none”, # Quitar la leyenda strip.text = element_text(face = “bold”, size = 12), # Estilo de los títulos de las facetas plot.title = element_text(face = “bold”, size = 12, hjust = 0.5), # Estilo del título plot.subtitle = element_text(size = 12, hjust = 0.5), # Estilo del subtítulo axis.text = element_text(size = 10, angle = 30) )+ guides(alpha = “none”) ``` |
::: {.cell-output-display}  ::: ::: |
| ## Ejercicios extras Marcos # ¿Qué 10 partidos obtuvieron mejores resultados en comparación con las predicciones de las encuestas? |
| ::: {.cell} |
{.r .cell-code} comparativa_partidos <- surveys_clean_siglas |> mutate(anno = year(date_elec)) |> group_by(anno, Partido) |> summarise( mean_intention = mean(value, na.rm = TRUE), .groups = "drop" ) |> mutate(percentage_intention = mean_intention / sum(mean_intention) * 100) |> inner_join(votes_percentage, by = c("anno", "Partido" = "siglas")) |> mutate(diferencia = percentage_votes - percentage_intention) |> arrange(desc(diferencia)) |> slice_max(order_by = diferencia, n = 10) ::: |
| # ¿Cuál es la relación entre el tamaño del censo y la fragmentación del voto (índice de Herfindahl)? Representar los valores de fragmentación en un mapa por comunidades autonomas. |
| ::: {.cell} |
| ```{.r .cell-code} library(mapSpain) library(ggplot2) library(dplyr) |
| election_tidy_with_siglas <- election_tidy_with_siglas |> mutate(codigo_ccaa = substr(codigo_ccaa, 1, 2)) |
| fragmentacion_ccaa <- election_tidy_with_siglas |> group_by(codigo_ccaa, Partido) |> summarise(votos_totales = sum(votos, na.rm = TRUE), censo_total = sum(censo, na.rm = TRUE), .groups = “drop”) |> group_by(codigo_ccaa) |> reframe( indice_herfindahl = sum((votos_totales / sum(votos_totales, na.rm = TRUE))^2, na.rm = TRUE), censo_total = unique(censo_total) ) |
| mapa_ccaa <- mapSpain::esp_get_ccaa() |
| mapa_fragmentacion_ccaa <- mapa_ccaa |> left_join(fragmentacion_ccaa, by = c(“codauto” = “codigo_ccaa”)) |
| ggplot(mapa_fragmentacion_ccaa) + geom_sf(aes(fill = indice_herfindahl), color = “gray90”, size = 0.1) + geom_sf_text( data = mapa_fragmentacion_ccaa |> filter(!is.na(indice_herfindahl)), aes(label = sprintf(“%.2f”, indice_herfindahl)), size = 2, color = “black”, fontface = “bold” ) + scale_fill_gradientn( colors = c(“lightcoral”, “gold”, “forestgreen”), name = “Fragmentación”, limits = c(0, 1), labels = scales::percent_format(accuracy = 1) ) + labs( title = “Fragmentación del Voto por Comunidades Autonomas en España”, subtitle = “Índice de Herfindahl”, fill = “Fragmentación (Herfindahl)” ) + coord_sf(datum = NA) + theme_minimal() + theme( plot.title = element_text(hjust = 0.5, size = 14, face = “bold”), plot.subtitle = element_text(hjust = 0.5, size = 12), legend.position = “right”, legend.title = element_text(size = 10), legend.text = element_text(size = 8), panel.grid = element_blank() ) ``` |
::: {.cell-output-display}  ::: ::: |
| ::: {.cell} |
| ```{.r .cell-code} # Rodri crecimiento_partido <- function(election_tidy) { # Calcular los votos totales por partido y año votos_agrupados <- election_tidy |> group_by(anno, Partido) |> summarise(total_votos = sum(votos, na.rm = TRUE), .groups = “drop”) |
| # Ordenar los datos por partido y año votos_ordenados <- votos_agrupados |> arrange(Partido, anno) |
| # Calcular el crecimiento o disminución porcentual entre elecciones consecutivas # Usamos lag() para obtener los votos del año anterior # En las elecciones en las que un determinado partido aparece por primera vez, el cambio # porcentual es 0 crecimiento <- votos_ordenados |> group_by(Partido) |> mutate(cambio_pct = ifelse(is.na(lag(total_votos)), 0, (total_votos - lag(total_votos)) / lag(total_votos) * 100)) |> ungroup() |
| return(crecimiento) } ``` ::: |
| ## Ejercicios extra Rodrigo # ¿Qué comunidad autónoma tiene la mayor participación electoral promedio en las elecciones entre 2008 y 2019? |
| ::: {.cell} |
| ```{.r .cell-code} codigo_a_comunidad <- c( “14” = “País Vasco”, “07” = “Castilla La Mancha”, “17” = “Comunidad Valenciana”, “01” = “Andalucía”, “08” = “Castilla y León”, “10” = “Extremadura”, “04” = “Islas Baleares”, “09” = “Cataluña”, “11” = “Galicia”, “02” = “Aragón”, “16” = “La Rioja”, “12” = “Madrid”, “15” = “Murcia”, “13” = “Navarra”, “03” = “Asturias”, “05” = “Canarias”, “06” = “Cantabria”, “18” = “Ceuta”, “19” = “Melilla” ) |
| participacion_media_ccaa <- election_tidy_with_siglas |> group_by(codigo_ccaa) |> summarise( participacion_promedio = mean(votos_candidaturas, na.rm = TRUE) / mean(censo, na.rm = TRUE) * 100 ) |
| participacion_media_ccaa <- participacion_media_ccaa |> mutate(nombre_ccaa = codigo_a_comunidad[as.character(codigo_ccaa)]) |
| participacion <- ggplot(participacion_media_ccaa, aes(x = reorder(nombre_ccaa, -participacion_promedio), y = participacion_promedio, fill = nombre_ccaa)) + geom_bar(stat = “identity”, show.legend = FALSE, width = 0.8) + scale_fill_viridis_d(option = “D”, begin = 0.2, end = 0.8) + scale_y_continuous(limits = c(0, 100)) + labs( title = “Comunidad Autónoma con Mayor Participación Electoral Promedio (2008-2019)”, subtitle = “Participación promedio en las elecciones durante el período 2008-2019”, x = “Comunidad Autónoma”, y = “Participación Promedio (%)” ) + theme_minimal(base_size = 15) + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 13, family = “Arial”, color = “darkblue”), axis.text.y = element_text(size = 13, family = “Arial”, color = “darkblue”), axis.title.x = element_text(size = 14, face = “bold”, family = “Arial”, color = “darkred”), axis.title.y = element_text(size = 14, face = “bold”, family = “Arial”, color = “darkred”), plot.title = element_text(size = 11, face = “bold”, family = “Arial”, color = “darkgreen”), plot.subtitle = element_text(size = 12, family = “Arial”, color = “gray50”), plot.margin = margin(10, 20, 10, 20), panel.grid.major = element_line(color = “gray85”, size = 0.5), panel.grid.minor = element_blank(), panel.background = element_rect(fill = “white”, color = “white”)) |
| participacion_int <- ggplotly(participacion) participacion_int ``` |
| ::: {.cell-output-display} |
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| ::: ::: |
#¿Cuál es la relación entre la participación electoral y el tamaño de la muestra de las encuestas?
encuestas_filtradas <- surveys_clean_siglas |>
filter(size >= 500)
participacion_por_partido <- encuestas_filtradas |>
group_by(Partido) |>
summarise(mean_participacion = mean(value, na.rm = TRUE))
partidos_colores <- c(
"PSOE" = "#D50032",
"PP" = "#0056A0",
"C's" = "#FF6600",
"VOX" = "#006747",
"UP" = "#9B4F96",
"ERC" = "#D82B6D",
"PNV" = "#006A3E",
"MP" = "#03A9F4",
"EH-BILDU" = "#E32B5F",
"CIU" = "#A7C3A4",
"BNG" = "#E60012",
"Otros" = "#BDBDBD"
)
encuestas <- ggplot(participacion_por_partido, aes(x = reorder(Partido, -mean_participacion), y = mean_participacion, fill = Partido)) +
geom_bar(stat = "identity", width = 0.7) +
scale_fill_manual(values = partidos_colores) +
labs(title = "Participación Electoral Promedio por Partido (Tamaño de Muestra > 500)",
x = "Partido", y = "Participación Electoral Promedio (%)") +
theme_minimal(base_size = 14) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()
) +
coord_flip()
encuestas_int <- ggplotly(encuestas)
encuestas_int